scipy.optimize.OptimizeResult¶

class
scipy.optimize.
OptimizeResult
[source]¶ Represents the optimization result.
Notes
There may be additional attributes not listed above depending of the specific solver. Since this class is essentially a subclass of dict with attribute accessors, one can see which attributes are available using the keys() method.
Attributes:  x : ndarray
The solution of the optimization.
 success : bool
Whether or not the optimizer exited successfully.
 status : int
Termination status of the optimizer. Its value depends on the underlying solver. Refer to message for details.
 message : str
Description of the cause of the termination.
 fun, jac, hess: ndarray
Values of objective function, its Jacobian and its Hessian (if available). The Hessians may be approximations, see the documentation of the function in question.
 hess_inv : object
Inverse of the objective function’s Hessian; may be an approximation. Not available for all solvers. The type of this attribute may be either np.ndarray or scipy.sparse.linalg.LinearOperator.
 nfev, njev, nhev : int
Number of evaluations of the objective functions and of its Jacobian and Hessian.
 nit : int
Number of iterations performed by the optimizer.
 maxcv : float
The maximum constraint violation.
Methods
clear
()copy
()fromkeys
($type, iterable[, value])Returns a new dict with keys from iterable and values equal to value. get
(k[,d])items
()keys
()pop
(k[,d])If key is not found, d is returned if given, otherwise KeyError is raised popitem
()2tuple; but raise KeyError if D is empty. setdefault
(k[,d])update
([E, ]**F)If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] values
()